FC-SVM: DNA binding Proteins prediction with Average Blocks (AB) descriptors using SVM with FC feature Selection

Achmad Ridok; Nashi Widodo; Wayan Firdaus Mahmudy; Muhaimin Rifai

doi:10.1109/SIET48054.2019.8986070

FC-SVM: DNA binding Proteins prediction with Average Blocks (AB) descriptors using SVM with FC feature Selection

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

DNA binding proteins have important functions and roles in various biological processes, namely regulation of transcription, DNA replication, DNA packaging, DNA repairs, and DNA rearrangement. More than 135,000 atomic-level biomolecular structures from experimental results have been stored in the Protein Data Bank (PDB) database. Therefore, we need computational methods that can predict quickly and accurately the existence of DNA binding proteins. This research proposes a new method FC-SVM that combine Support Vector Machine (SVM) with F-score (FC) feature selection method to identify DNA binding protein using average block (AB) descriptor that was extracted from position specific scoring matrix (PSSM). Evaluation of the proposed method with 10 cross validations in three datasets of PDB186, PDB594 and PDB1075 shows the results of the performance 0.66, 0.72 and 0.75 resoectively.

Original language	English
Title of host publication	Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	22-27
Number of pages	6
ISBN (Electronic)	9781728138787
DOIs	https://doi.org/10.1109/SIET48054.2019.8986070
Publication status	Published - Sept 2019
Event	4th International Conference on Sustainable Information Engineering and Technology, SIET 2019 - Lombok, Indonesia Duration: 28 Sept 2019 → 30 Sept 2019

Publication series

Name	Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019

Conference

Conference	4th International Conference on Sustainable Information Engineering and Technology, SIET 2019
Country/Territory	Indonesia
City	Lombok
Period	28/09/19 → 30/09/19

Keywords

average block descriptor
DNA binding protein
F-score feature selection
PSSM
SVM

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10.1109/SIET48054.2019.8986070

Cite this

Ridok, A., Widodo, N., Mahmudy, W. F., & Rifai, M. (2019). FC-SVM: DNA binding Proteins prediction with Average Blocks (AB) descriptors using SVM with FC feature Selection. In Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019 (pp. 22-27). Article 8986070 (Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIET48054.2019.8986070

Ridok, Achmad ; Widodo, Nashi ; Mahmudy, Wayan Firdaus et al. / FC-SVM : DNA binding Proteins prediction with Average Blocks (AB) descriptors using SVM with FC feature Selection. Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 22-27 (Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019).

@inproceedings{9521ae34808c403c87203f48bf4062ed,

title = "FC-SVM: DNA binding Proteins prediction with Average Blocks (AB) descriptors using SVM with FC feature Selection",

abstract = "DNA binding proteins have important functions and roles in various biological processes, namely regulation of transcription, DNA replication, DNA packaging, DNA repairs, and DNA rearrangement. More than 135,000 atomic-level biomolecular structures from experimental results have been stored in the Protein Data Bank (PDB) database. Therefore, we need computational methods that can predict quickly and accurately the existence of DNA binding proteins. This research proposes a new method FC-SVM that combine Support Vector Machine (SVM) with F-score (FC) feature selection method to identify DNA binding protein using average block (AB) descriptor that was extracted from position specific scoring matrix (PSSM). Evaluation of the proposed method with 10 cross validations in three datasets of PDB186, PDB594 and PDB1075 shows the results of the performance 0.66, 0.72 and 0.75 resoectively.",

keywords = "average block descriptor, DNA binding protein, F-score feature selection, PSSM, SVM",

author = "Achmad Ridok and Nashi Widodo and Mahmudy, \{Wayan Firdaus\} and Muhaimin Rifai",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019 ; Conference date: 28-09-2019 Through 30-09-2019",

year = "2019",

month = sep,

doi = "10.1109/SIET48054.2019.8986070",

language = "English",

series = "Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "22--27",

booktitle = "Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019",

}

Ridok, A , Widodo, N , Mahmudy, WF & Rifai, M 2019, FC-SVM: DNA binding Proteins prediction with Average Blocks (AB) descriptors using SVM with FC feature Selection. in Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019., 8986070, Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019, Institute of Electrical and Electronics Engineers Inc., pp. 22-27, 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019, Lombok, Indonesia, 28/09/19. https://doi.org/10.1109/SIET48054.2019.8986070

FC-SVM: DNA binding Proteins prediction with Average Blocks (AB) descriptors using SVM with FC feature Selection. / Ridok, Achmad ; Widodo, Nashi ; Mahmudy, Wayan Firdaus et al.
Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 22-27 8986070 (Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - FC-SVM

T2 - 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019

AU - Ridok, Achmad

AU - Widodo, Nashi

AU - Mahmudy, Wayan Firdaus

AU - Rifai, Muhaimin

PY - 2019/9

Y1 - 2019/9

N2 - DNA binding proteins have important functions and roles in various biological processes, namely regulation of transcription, DNA replication, DNA packaging, DNA repairs, and DNA rearrangement. More than 135,000 atomic-level biomolecular structures from experimental results have been stored in the Protein Data Bank (PDB) database. Therefore, we need computational methods that can predict quickly and accurately the existence of DNA binding proteins. This research proposes a new method FC-SVM that combine Support Vector Machine (SVM) with F-score (FC) feature selection method to identify DNA binding protein using average block (AB) descriptor that was extracted from position specific scoring matrix (PSSM). Evaluation of the proposed method with 10 cross validations in three datasets of PDB186, PDB594 and PDB1075 shows the results of the performance 0.66, 0.72 and 0.75 resoectively.

AB - DNA binding proteins have important functions and roles in various biological processes, namely regulation of transcription, DNA replication, DNA packaging, DNA repairs, and DNA rearrangement. More than 135,000 atomic-level biomolecular structures from experimental results have been stored in the Protein Data Bank (PDB) database. Therefore, we need computational methods that can predict quickly and accurately the existence of DNA binding proteins. This research proposes a new method FC-SVM that combine Support Vector Machine (SVM) with F-score (FC) feature selection method to identify DNA binding protein using average block (AB) descriptor that was extracted from position specific scoring matrix (PSSM). Evaluation of the proposed method with 10 cross validations in three datasets of PDB186, PDB594 and PDB1075 shows the results of the performance 0.66, 0.72 and 0.75 resoectively.

KW - average block descriptor

KW - DNA binding protein

KW - F-score feature selection

KW - PSSM

KW - SVM

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DO - 10.1109/SIET48054.2019.8986070

M3 - Conference contribution

AN - SCOPUS:85080121328

T3 - Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019

SP - 22

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BT - Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 28 September 2019 through 30 September 2019

ER -

Ridok A , Widodo N , Mahmudy WF , Rifai M. FC-SVM: DNA binding Proteins prediction with Average Blocks (AB) descriptors using SVM with FC feature Selection. In Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 22-27. 8986070. (Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019). doi: 10.1109/SIET48054.2019.8986070

FC-SVM: DNA binding Proteins prediction with Average Blocks (AB) descriptors using SVM with FC feature Selection

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Keywords

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